Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract ideal without significantly more. The claims are directed to the abstract idea of a mental process. This judicial exception is not integrated into a practical application nor amount so significantly more because the additional elements alone or in combination are mere insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea.
Claim 1
Step 1: The claim recites a system, therefore, it falls into the statutory category of an apparatus.
Step 2A Prong 1: The claim recites, inter alia:
generate building recommendations based on recommendation requests; (This amounts to a mental process of judgement and evaluation wherein a user gives recommendations regarding a building.)
generating, using feedback data, a second model of a second model type different than the first model type; (This is mental step of judgment and evaluation wherein a user gets feedback on the first recommendations they made user, wherein the first recommendations where made without any information about the users preferences, then based on persons feedback the user creates a different model or way of generating recommendations to a user based on their preferences, thus it’s a different model type for recommendations. Similar to someone asking for a movie recommendation and the user suggest a marvel movie as it biggest movie in theaters at the time. The person tells the user they are not comic movie fans and like horror movies, so the now the use changes their suggest based on user feedback to scary movie.)
transitioning from generating recommendations by the first model to a second model of the second model type by comparing performance of the first model type to the second model based on the feedback data. (This amounts to a mental process of judgement and evaluation wherein a user compares the performance data of two models, and chooses which is better and to use.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
with a model of a first model type designed to operate without stating data; and using a first and second model for recommendations; (These limitations are cited at high level of generality and result in using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
receive feedback data on the recommendation requests generated by the model of the first model type; and (This limitation amount to data gathering, which is extra-solution activity, see MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “receive feedback data on the recommendation requests generated by the model of the first model type;” amount to data gathering or transmitting data and is well-understood, routine and conventional and does not amount to significantly more. See MPEP 2106.06(d)(II) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”. The limitations “with a model of a first model type designed to operate without stating data;” and using a first and second model of a second model type different than the first model type amounts training and using of a machine learning models and amounts to using machine learning as tool to apply an abstract idea, see MPEP 2106.05(f).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Claim 2
Step 1: The claim recites a system, therefore, it falls into the statutory category of an apparatus.
Step 2A Prong 1: The claim recites, inter alia:
Comparing a first performance of the second model of the first model type and a second performance of the second model of the second model type to a performance of the evolving reference. (This amount to a mental process of judgement and evaluation wherein a user compares the performance data of 3 different models to see which performs better.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim does not recite any additional elements.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim does not recite any additional elements.
Claim 3
Step 1: The claim recites a system, therefore, it falls into the statutory category of an apparatus.
Step 2A Prong 1: The claim recites, inter alia:
Partition the feedback data into a first data set and a second data set; (This amount to a mental process of user judgement and evaluation and can be done with the aid of pen and paper. It amounts to a user consider a feedback data set and dividing it into two different sets, can be done with the aid of pen and paper.)
generate first recommendations for recommendation requests; and generating second recommendations for the recommendation request; (This amounts to a mental process of judgement and evaluation wherein a user gives recommendations regarding a building.)
comparing performance of the second model of the first model type and the second model of the second model and an evolving reference model (This amounts to a mental process of judgement and evaluation wherein a user compares the performance data of three models based on rewards, and chooses which is better and to use.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
Using the second data set with a second model of the first model type, the second model of the first model type generated based on at least a portion of the first data set to generating recommendations, and using a second data set with a second model of the second model type, the second model of the second model type generated based on at least the portion of the first data set; (These limitations are cited at high level of generality and result in using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
generating rewards of the second model and the second model with the first recommendations, the second recommendations, and the evolving reference model of the first model type, wherein the evolving reference model of is based on the first data set and the second data set. (This limitation amounts to the using generic hardware to execute the abstract idea as it amounts to using second model of first type, second model of second type and evolving model that generates rewards as result of the it use.)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are a combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations of “Using the second data set with a second model of the first model type, the second model of the first model type generated based on at least a portion of the first data set to generating recommendations, and using a second data set with a second model of the second model type, the second model of the second model type generated based on at least the portion of the first data set;” and “generating rewards of the second model and the second model with the first recommendations, the second recommendations, and the evolving reference model of the first model type, wherein the evolving reference model of is based on the first data set and the second data set” amounts to using machine learning models as tool to apply an abstract idea, see MPEP 2106.05(f).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are a combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
4. The building system of claim 3, wherein the second model of the first model type and the evolving reference model are both an evolving matrix method (EMM) model; wherein the second model of the second model type is a collaborative filtering (CF) model. (The claim limitation amounts to specifying the type of models used being evolving matrix models and collaborative filtering, however the claims amount are cited a high level of generality and result in using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
5. The building system of claim 1, wherein the instructions cause the one or more processors to: generate the building recommendations with a matrix of an evolving matrix method (EMM) and a plurality of values of the matrix to be updated over time as the feedback data is collected. (This amount to saving data to memory wherein the matrix is updated by saving the feedback to the matrix, thus is extra-solution activity of storing and retrieving information in memory, see MPEP 2106.05(g). Also, it well-understood, routine and conventional, see MPEP 2106.05(d)(II)(iv) wherein the courts have recognized the following computer functions as well-understood, routine, and conventional functions wherein they are claim in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. The processor being used amounts to using a generic computer hardware to execute the abstract idea, see MPEP 2106.05(f). The generating building recommendation is a mental processing of judgment and evaluation wherein a user gives recommendations. )
6. The building system of claim 5, wherein the matrix comprises rows and columns, wherein one of the rows or the columns represent a plurality of users and one of the rows or the columns represent a plurality of possible recommendations. (This amounts to the data extra-solution activity of the particular type of data to manipulated, see MPEP 2106.05(g).)
Claim 7
Step 1: The claim recites a system, therefore, it falls into the statutory category of an apparatus.
Step 2A Prong 1: The claim recites, inter alia:
generate a score for each of the plurality of possible recommendations based on one or more of the plurality of values, the one or more of the plurality of values associated with the one or more users; and (This amounts to a mental process of evaluation and judgement wherein a user considers possible items or things to recommend to a person and assigns each item or things a rank or score based on user. So, I know a user likes a room to be warmer and thus I suggest possible temperatures to make the roomer wherein I score the possible temperatures based on the temperatures being higher or not.)
select a possible recommendation associated with a highest score from the plurality of possible recommendations. (This limitation amounts to a mental process of judgement and evaluation wherein a user chooses what recommendation to give based on the scores or ranks for each recommendation.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
receive a recommendation request associated with one or more users; (This limitation amount to data gathering, which is extra-solution activity, see MPEP 2106.05(g).)
wherein each intersection of rows and the columns represent the plurality of values; (extra-solution activity of particular type of data to be manipulated, see MPEP 2106.05(g)
wherein the instructions cause the one or more processors to: (this amounts to using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are a combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “receive a recommendation request associated with one or more users;” amount to data gathering or transmitting data and is well-understood, routine and conventional and does not amount to significantly more. See MPEP 2106.06(d)(II) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”. The limitation “wherein the instructions cause the one or more processors to:” amounts to using generic computer components to apply an abstract idea, see MPEP 2106.05(f). The intersection of rows and columns represent the plurality of values is extra-solution activity of particular type of data to be manipulated, see MPEP 2106.05(g).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Claim 8
Step 1: The claim recites a system, therefore, it falls into the statutory category of an apparatus.
Step 2A Prong 1: The claim recites, inter alia:
transition from generating the second building recommendations by the second model of the second model type to a third model of a third model type. (This amounts to a mental process of judgement and evaluation wherein a user compares the performance data of two models, and chooses which is better and to use.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
receive second feedback data on second building recommendations generated by the second model of the second model type; and (This limitation amount to data gathering, which is extra-solution activity, see MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are a combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations of “receive second feedback data on second building recommendations generated by the second model of the second model type;” amount to data gathering or transmitting data and is well-understood, routine and conventional and does not amount to significantly more. See MPEP 2106.06(d)(II) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are a combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
9. The building system of claim 8, wherein the second model of the second model type is a collaborative filtering (CF) model and the third model of the third model type is a supervised learning (SL) model. (The claim limitation amounts to specifying the type of models used being a supervised learning model and a collaborative filtering model, however the claim is cited a high level of generality and result in using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
Claim 10
Step 1: The claim recites a system, therefore, it falls into the statutory category of an apparatus.
Step 2A Prong 1: The claim recites, inter alia:
transition from generating the third building recommendations by the third model of the second model type to a fourth model of a fourth model type. (This amounts to a mental process of judgement and evaluation wherein a user compares the performance data of two models, and chooses which is better and to use.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
receive third feedback data on third building recommendations generated by the third model of the third model type; and (This limitation amount to data gathering, which is extra-solution activity, see MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are a combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations of “receive third feedback data on third building recommendations generated by the third model of the third model type;” amount to data gathering or transmitting data and is well-understood, routine and conventional and does not amount to significantly more. See MPEP 2106.06(d)(II) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are a combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
11. The building system of claim 10, wherein the third model of the third model type is a supervised learning (SL) model and the fourth model of the fourth model type is a reinforcement learning (RL) model. (The claim limitation amounts to specifying the type of models used being a supervised learning model and a reinforcement learning model, however the claim is cited a high level of generality and result in using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
Claim 12 is the method embodiment of claim 1 with similar limitations to that claim 1, as such claim 12 is rejected using the same reasoning found in claim 1.
Claim 13 is the method embodiment of claim 2 with similar limitations to that claim 2, as such the claim is rejected using the same reasoning found in claim 2.
Claim 14 is the method embodiment of claim 8 with similar limitations to that claim 8, as such the claim is rejected using the same reasoning found in claim 8.
Claim 15 is the method embodiment of claim 3 with similar limitations to that claim 3, as such the claim is rejected using the same reasoning found in claim 3.
Claim 16 is the method embodiment of claim 4 with similar limitations to that claim 4, as such the claim is rejected using the same reasoning found in claim 4.
Claim 17 is the method embodiment of claim 5 with similar limitations to that claim 5, as such the claim is rejected using the same reasoning found in claim 5.
Claim 18 is the method embodiment of claim 6 with similar limitations to that claim 6, as such the claim is rejected using the same reasoning found in claim 6.
Claim 19 is the method embodiment of claim 7 with similar limitations to that claim 7, as such the claim is rejected using the same reasoning found in claim 7.
Claim 20
Step 1: The claim recites a system, therefore, it falls into the statutory category of an apparatus.
Step 2A Prong 1: The claim recites, inter alia:
generate building recommendations based on recommendation requests with a matrix, wherein one of a row or column of the matrix represents a plurality of users and one or more of the row or the column represent a plurality of recommendations; (This amounts to a mental process of judgement and evaluation wherein a user looks a matrix of users and items (recommendations) to find recommendations regarding a building and user to give to a user. For example, looking at the matrix the user likes to have separate heating and cooling in every room, so a user would suggest have multiple ac units wherein each room its only ac controls.)
generate, using the feedback data, a second model of a model type different from the first model; (This is mental step of judgment and evaluation wherein a user gets feedback on the first recommendations they made user, wherein the first recommendations where made without any information about the users preferences, then based on persons feedback the user creates a different model or way of generating recommendations to a user based on their preferences, thus it’s a different model type for recommendations. Similar to someone asking for a movie recommendation and the user suggest a marvel movie as it biggest movie in theaters at the time. The person tells the user they are not comic movie fans and like horror movies, so the now the use changes their suggest based on user feedback to scary movie.)
compare performance of the second model type against the EMM using the second model of the second model type and the second matrix of the EMM; and (This is a mental step of observation, evaluation and judgment wherein a user compares the performance data various models.)
transitioning from generating recommendations by the first model to a second model of the second model type by comparing performance of the first model type to the second model based on the comparison. (This amounts to a mental process of judgement and evaluation wherein a user compares the performance data of two models, and chooses which is better and to use.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
Using a matrix of an evolving matrix method (EMM), designed to operate without starting data when no starting data is available; using a second matrix of the EMM and using a second model of second model type; (These limitations are cited at high level of generality and result in using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
receive feedback data on the recommendation requests generated by matrix; and (This limitation amount to data gathering, which is extra-solution activity, see MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element of “receive feedback data on the recommendation requests generated by the matrix;” amount to data gathering or transmitting data and is well-understood, routine and conventional and does not amount to significantly more. See MPEP 2106.06(d)(II) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
The limitations of “Using a matrix of an evolving matrix method (EMM), designed to operate without starting data when no starting data is available; using a second matrix of the EMM and using a second model of second model type” amounts to using the machine learning modesl (EMM and second model of a second type) as tool to apply an abstract idea, see MPEP 2106.05(f).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
21. The building system of claim 1, wherein the instructions cause the one or more processors to: generate the second model by training the second model with the feedback data. (This is cited at high level of generality and results in mere instruction to implement an abstract idea on a computer wherein the computer is a tool to perform the abstract idea, see MPEP 2106.05(f).)
22. The building system of claim 1, wherein the instructions cause the one or more processors to: generate the second model by a supervised learning technique or a reinforcement learning technique that trains the second model with the feedback data. (This is cited at high level of generality and results in mere instruction to implement an abstract idea on a computer wherein the computer is a tool to perform the abstract idea, see MPEP 2106.05(f).)
23. The building system of claim 1, wherein the instructions cause the one or more processors to: generate the second model by producing a matrix using the feedback data. (This is cited at high level of generality and results in mere instruction to implement an abstract idea on a computer wherein the computer is a tool to perform the abstract idea, see MPEP 2106.05(f).)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 12-13 and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al., ("Collaborative Filtering and Deep Learning Based Recommendation System for Cold Start Items" - hereinafter Wei) in view of Haslett et al., (US2018/0081330A1 - hereinafter Haslett) and further in vie of Ghazanfar et al. (“An Improved Switching Hybrid Recommender System Using Naive Bayes Classifier and Collaborative Filtering” – hereinafter Ghazanfar).
In regards to claim 1, Wei discloses generate building recommendations based on recommendation requests with a model of a first model type designed to operate without starting data when the starting data is unavailable; (Wei page 31 section 3 – page 32 first paragraph teaches a recommendation system that generates recommendation in cold start and incomplete cold start situations, which are situations for when there is no training data (unavailable) or scarce training data. This is also stated in the abstract.) and
receive feedback data on the recommendation requests generated by the model of the first model type; and (Wei page 29 last paragraph cites “Instead, it relies on the relationship between users and items, which are typically encoded in a rating feedback matrix with each element representing a specific user rating on a specific item.” From this we see the rating is feedback. Then on page 32 left column first paragraph it teaches getting user ratings, thus feedback. Also, page 32 section 3.2 second paragraph teaches implicit feedback. Thus, user feedback from recommendation of first model.)
However, Wei does not disclose a building system comprising one or more memory devices configured to store instructions thereon, that, when executed by one or more processors, cause the one or more processors to generate building recommendations and transition from generating the building recommendations by the model of the first model type to a second model of a second model type by comparing performance of the first model type to the second model type based on comparison.
Haslett discloses a building system comprising one or more memory devices configured to store instructions thereon, that, when executed by one or more processors, cause the one or more processors to: (Haslett para. [0057] teaches a processor and para. [0156] teaches memory and the abstract teaches a building environment management system.)
generate building recommendations (Haslett para. [0080-0081] teaches building recommendations as it recommends heating levels for rooms of a building), generating a second model of the first type based on feedback and transition from generating the building recommendations by the model of the first model type to a second model of the first model type by comparing performance of the first model type to the second model of the first type based on the feedback data. (Haslett fig. 3, element 34, 38, 40, 42, 44a, 44b, and 46, as well as para. [0143-0156] wherein a first model is trained and makes prediction (recommendation), then the error of the prediction is found by mean square error (MSE) when actual data is found. The system them compares the first model (old model) to a new model by comparing the MSE to see which error is lower, if the new model has lower MSE the system transition to it and it then becomes the old model.)
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to modify the teachings of the Wei with that of Haslett in order to allow for generating building recommendation and transition between a first and second model as both references deal with making predictions using machine learning models. The benefit of doing so it creates a recommendation system that operate in cold start situations and creates an accurate and efficient prediction system based on feedback.
Wei in view of Haslett does not disclose generating, using the feedback data, a second model of a second model type different than a first model type.
Ghazanfar discloses using generating, using the feedback data, a second model of a second model type different than a first model type and comparing the performance of the second model of the first type to the second model of the second type. (Ghazanfar in the abstract disclose a hybrid recommender system that uses naïve Bayes Classifier (NB) and collaborative filtering (CF) together. Then on page 5 section 4 last paragraph and footnote 17 teaches that in situations of cold start CF will fail due no starting data and in this case NB is used. Thus, NB system is designed for situations when no starting data is available. when no starting data is available. Section 3. 1 teaches getting user rating (feedback) on items (recommendation) and is used to update or create the collaborative filtering system. Page 5 section 4 teaches determining when to switch between system based on cold start (first and second model of the first type) and confidence values.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Wei in view of Haslett with that Ghazanfar in order to allow using two different types of model as all the references deal with recommendation system and the benefit of doing so it allows for creating a more accurate and efficient system by taking advantage of naïve bayes when no starting data is available and transitioning to collaborative filtering with enough data is present creating a system that covers all situations.
In regards to claim 12 it is the method of claim 1 with similar limitations and thus rejected using the same reasoning found in claim 1.
In regards to claim 21, Wei in view of Haslett with that Ghazanfar disclose the building system of claim 1, wherein the instructions cause the one or more processors to: generate the second model by training the second model with the feedback data. (Wei page 34 second paragraph teaches updating the model, collaborative filtering, based user rates, thus it training the model using feedback data.)
In regards to claim 22, Wei in view of Haslett with that Ghazanfar disclose the building system of claim 1, wherein the instructions cause the one or more processors to: generate the second model by a supervised learning technique or a reinforcement learning technique that trains the second model with the feedback data. (Wei page 34 second paragraph teaches updating the model, collaborative filtering, based user rates, thus it training or generating the model using feedback data, wherein this is a form of supervised learning.)
Claims 5-7, 17-19 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al., ("Collaborative Filtering and Deep Learning Based Recommendation System for Cold Start Items" - hereinafter Wei) in view of Haslett et al., (US2018/0081330A1 - hereinafter Haslett) in view of Ghazanfar et al. (“An Improved Switching Hybrid Recommender System Using Naive Bayes Classifier and Collaborative Filtering” – hereinafter Ghazanfar) and further in view of Yu (US2017/0148083 A1).
In regard to claim 5, Wei in view of Haslett in view of Ghazanfar discloses the building system of claim 1, wherein the instructions cause the one or more processor to generate the building recommendations, (Haslett para. [0057] teaches a processor and para. [0156] teaches memory and the abstract teaches a building environment management system. Haslett para. [0080-0081] teaches building recommendations as it recommends heating levels for rooms of a building), but does not disclose wherein the model is a matrix of an evolving matrix method (EMM); and wherein the instructions cause a plurality of values of the matrix to be updated overtime as the feedback data is collected.
Yu discloses wherein the model is a matrix of an evolving matrix method; (Yu table 3 and para. [0038] teaches a user-item matrix and table 4 and para. [0039] shows its evolving as it has been filled in) and wherein the instructions cause a plurality of values of the matrix to be updated overtime as the feedback data is collected. (Yu para. [0045] cites “By comparing Eq. (3) with Eq. (2), it can be seen that the prediction algorithm by Eq. (3) does not handle all the users and items but only the user who just provided their feedback. This will further reduce the time spent on updating the user-item matrix while reducing the response time to the user's feedback.” This teaches updating the user-item matrix, which holds the values, based on a user feedback being collected. Para. [0051] also teaches updating a user-item rating matrix and para. [0046 and 0056] waiting for user feedback to update.)
It would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Wei in view Haslett in view of Ghazanfar with that of Yu in order to allow for updating a user-time rating matrix based on user feedback as all the references deal with user recommendation system and the benefit of doing so it allow more accurately recommendation by taking into account the user feedback on recommendations.
In regards to claim 6, Wei in view of Haslett in view of Ghazanfar in view of Yu discloses the building system of claim 5, wherein the matrix comprises rows and columns, wherein one of the rows or the columns represent a plurality of users and one of the rows and the columns represent a plurality of possible recommendations. (Yu table 3 and table 4 shows a user-item matrix wherein the row at the top is the items and the column on the left most side is the users. Also, the intersection of the row and column represents ratings and possible recommendations.)
In regards to claim 7, Wei in view of Haslett in view of Ghazanfar in view of Yu discloses the building system of claim 6, wherein each intersection of the rows and the columns represents the plurality of values; (Yu table 3)
wherein the instructions cause the one or more processors to: (Yu fig. 8 shows a processor (CPU element 801)
receive a recommendation request associated with one or more users; (Yu para. [0055] teaches a user logging into the system wherein they will get a recommendation based on their received information. This is the received recommendation request wherein the user logs into the system. )
generate a score for each of the plurality of possible recommendations based on one or more of the plurality of values, the one or more of the plurality of values associated with the one or more users; and (Yu para. [0041] teaches predicting missing ratings values of items in the user-item matrix based on user vector and item vectors associated with the users.)
select a possible recommendation associated with a highest score from the plurality of possible recommendations. (Yu para. [0050] teaches the interesting items could be recommended to the user based on the ranking of the rating values in the predicted user-item rating matrix. Also claim 7 last limitation cites recommending items based on ranking of the predicted rating value, thus recommends the highest score.)
In regards to claim 17, it is the method embodiment of claim 5 with similar limitations to that claim 5, thus claim 17 is rejected using the same reasoning found in claim 5.
Claim 18 is the method embodiment of claim 6 with similar limitations to that claim 6, as such the claim is rejected using the same reasoning found in claim 6.
Claim 19 is the method embodiment of claim 7 with similar limitations to that claim 7, as such the claim is rejected using the same reasoning found in claim 7.
In regards to claim 23, Wei in view of Haslett with that Ghazanfar disclose the building system of claim 1, but does not explicitly disclose wherein the instructions cause the one or more processors to: generate the second model by producing a matrix using the feedback data.
Yu discloses generating a model by producing a matrix using feedback data. (Yu para. [0055] teaches a user logging into the system wherein they will get a recommendation based on their received information. Yu table 3 and 4 shows evolving matrix which is the evolving matrix method. Yu table 3 shows an user-time matrix wherein the users are the left most column represented by U, the items are top most row represent by I, and the possible recommendation and rating are wherein the columns and rows intersect. Yu para. [0045] cites “By comparing Eq. (3) with Eq. (2), it can be seen that the prediction algorithm by Eq. (3) does not handle all the users and items but only the user who just provided their feedback. This will further reduce the time spent on updating the user-item matrix while reducing the response time to the user's feedback.” This teaches updating the user-item matrix, which holds the values, based on a user feedback being collected. Para. [0051] also teaches updating a user-item rating matrix and para. [0046 and 0056] waiting for user feedback to update. As the matrix is being updated, so is the model wherein this would be generating the model using user feedback data.
It would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Wei in view Haslett in view of Ghazanfar with that of Yu in order to allow for generating a model by producing a matrix using user feedback as all the references deal with user recommendation system and the benefit of doing so it allow more accurately recommendation by taking into account the user feedback on recommendations.
Response to Arguments
Applicant’s arguments filed on 15 December 2025, with respect to the rejection of claims under 35 USC 112(b), have been fully considered and are persuasive. Therefore, the rejection has been withdrawn.
In regards to applicant’s argument under 35 USC 101 for being abstract idea the examiner does not find the argument’s persuasive. Applicant argues that instant applicant claims are similar that of example 47 and claim 3 limitations d-f of the AI Guidelines examples issued by the office. The examiner respectfully traverses this argument as the limitation d-f of that example are not similar to that of the instant claims. The instant claims do not detect a source address associated with one or more malicious network packets in real time, does not drop the one or more detected malicious network packets in time, nor block future traffic from the source address. The instant applicant does not even deal with malicious packet and source address detection, so they examiner does not believe the claims are similar as finds the argument not persuasive.
The applicant argues that amendment to the claims is an improvement to the function of the computer, the examiner respectfully traverses the applicant arguments. The amendment to the claim of the first model designed to operate without starting data is not an improvement to a computer system but rather to the abstract idea of making a recommendation. As such it is not an improvement to computer. Applicant also argues that amendment to claims does not include an abstract idea of mental processes that can practically be performed in the human mind, which the examiner respectfully traverses. The abstract idea found in the independent claims are:
generate building recommendations based on recommendation requests; (This amounts to a mental process of judgement and evaluation wherein a user gives recommendations regarding a building.)
generating, using feedback data, a second model of a second model type different than the first model type; (This is mental step of judgment and evaluation wherein a user gets feedback on the first recommendations they made user, wherein the first recommendations where made without any information about the users preferences, then based on persons feedback the user creates a different model or way of generating recommendations to a user based on their preferences, thus it’s a different model type for recommendations. Similar to someone asking for a movie recommendation and the user suggest a marvel movie as it biggest movie in theaters at the time. The person tells the user they are not comic movie fans and like horror movies, so the now the use changes their suggest based on user feedback to scary movie.)
transitioning from generating recommendations by the first model to a second model of the second model type by comparing performance of the first model type to the second model based on the feedback data. (This amounts to a mental process of judgement and evaluation wherein a user compares the performance data of two models, and chooses which is better and to use.)
As such the independent claims do contain an abstract idea. Lastly the applicant states that step 2B of the 101 abstract idea analysis is incorrect as the limitations of “generate building recommendations based on recommendation requests with a model of a first model type designed to operate without starting data,” “generate, using the feedback data, a second model of a second model type different than the first model type,” and “transition from generating the building recommendations by the model of the first model type to the second model of the second model type by comparing performance of the first model type to the second model type based on the feedback data” (claim 1) is not “well-understood, routine, conventional activity.” MPEP 2106.05(d)(1)(2). Furthermore, the limitations of independent claim 1 add a combination of additional elements that are not “well-understood, routine, conventional activity.” (MPEP 2106.05(d)(1)(2)). The examiner respectfully traverses the applicant argument as it was not stated that any of the are well-understood, routine or conventional. It was stated that “generate building recommendations based on recommendation requests”; and “transition from generating the building recommendations by the model of the first model type to the second model of the second model type by comparing performance of the first model type to the second model type based on the feedback data” are abstract ideas of observation, evaluation and judgement, wherein a user give a recommendation to a person based a request and no additional information. The user gets feedback on the first recommendations they made user, wherein the first recommendations were made without any information about the users preferences, then based on persons feedback the user creates a different model or way of generating recommendations to a user based on their preferences, thus it’s a different model type for recommendations. Similar to someone asking for a movie recommendation and the user suggest a marvel movie as it biggest movie in theaters at the time. The person tells the user they are not comic movie fans and like horror movies, so the now the use changes their suggest based on user feedback to scary movie. Thus, giving recommendation and transition to a different model based on feedback is a mental process. Also comparing the performance of two models is also a mental process of evaluation, wherein a user simply compares the results of two models. The use of models and generating a model are cites a high level of generality and result in using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f). The combination of these features and elements do not amount to significantly more nor integrate the abstract idea into a practical application as the additional elements as they are a combination of generic computer functions that are implemented to perform the disclosed abstract idea above. As such the examiner maintains the rejection under 35 USC 101 for being an abstract idea.
In regards to rejection under 35 USC 103, upon further review and consider that examiner found that the prior art did disclose the amended limitations of generating recommendations when starting data is unavailable, a second model of a first type, and comparing the performance of the second model of a first type to a second model of second type, as such the rejection under USC 103 is maintain. See the analysis above.
Conclusion
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/PAULINHO E SMITH/Primary Examiner, Art Unit 2127